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Brain Sci. 2019 Feb 6;9(2). pii: E34. doi: 10.3390/brainsci9020034.

The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control.

Author information

1
Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK. christopher.buckley2@newcastle.ac.uk.
2
Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK. Lisa.Alcock@newcastle.ac.uk.
3
Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK. R.Mc-Ardle2@newcastle.ac.uk.
4
Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK. Rana.zia-ur-Rehman@newcastle.ac.uk.
5
Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK. Silvia.Del-Din@newcastle.ac.uk.
6
Department of Mechanical Engineering, Sheffield University, Sheffield S1 3JD, UK. c.mazza@sheffield.ac.uk.
7
Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK. alison.yarnall@newcastle.ac.uk.
8
The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK. alison.yarnall@newcastle.ac.uk.
9
Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK. lynn.rochester@ncl.ac.uk.
10
The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK. lynn.rochester@ncl.ac.uk.

Abstract

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions-including Parkinson's disease, ataxia, and dementia-we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel 'big data' approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.

KEYWORDS:

Parkinson’s disease; ataxia; deep learning; dementia; disease phenotyping; machine learning; movement science; risk prediction

PMID:
30736374
DOI:
10.3390/brainsci9020034
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